Spatiotemporal Air Quality Inference of Low-Cost Sensor Data; Application on a Cycling Monitoring Network

Abstract

Air quality monitoring in heterogeneous cities is challenging as a high resolution in both space and time is required to accurately assess population exposure. As regulatory monitoring networks are sparse due to high investment and maintenance costs, recent advances in sensor and IoT technologies have resulted in innovative sensing approaches like mobile sensing to increase the spatial monitoring resolution. An example of such an opportunistic mobile monitoring network is “Snuffelfiets”, a project where air quality data is collected from mobile sensors attached to bicycles in Utrecht (NL). The collected data results in a sparse spatiotemporal matrix of measurements which can be completed using data-driven techniques. This work reports on the potential of two machine learning approaches to infer the collected air quality measurements in both space and time; a deep learning model based on Variational Graph Autoencoders (AVGAE) and a Geographical Random Forest model (GRF). A temporal validation exercise is performed at two regulatory monitoring stations following the FAIRMODE modelling quality objectives protocol. This work demonstrates the potential of data-driven techniques for spatiotemporal air quality inference of sensor data as the considered models performed well in terms of accuracy and correlation. The model observed performance metrics approach current state-of-the-art physical models in terms of performance while needing much lower resources, computational power, infrastructure and processing time.